Auto-adaptive Laplacian Pyramids

Ángela Fernández, Neta Rabin, Dalia Fishelov, José R. Dorronsoro

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An important challenge in Data Mining and Machine Learning is the proper analysis of a given dataset, especially for understanding and working with functions defined over it. In this paper we propose Auto-adaptive Laplacian Pyramids (ALP) for target function smoothing when the target function is defined on a high-dimensional dataset. The proposed algorithm automatically selects the optimal function resolution (stopping time) adapted to the data defined and its noise. We illustrate its application on a radiation forecasting example.

Original languageEnglish
Title of host publicationESANN 2016 - 24th European Symposium on Artificial Neural Networks
Publisheri6doc.com publication
Pages59-64
Number of pages6
ISBN (Electronic)9782875870278
StatePublished - 2016
Externally publishedYes
Event24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016 - Bruges, Belgium
Duration: 27 Apr 201629 Apr 2016

Publication series

NameESANN 2016 - 24th European Symposium on Artificial Neural Networks

Conference

Conference24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016
Country/TerritoryBelgium
CityBruges
Period27/04/1629/04/16

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